Bee disease Varroa prediction: utilizing convolutional neural networks with augmentation for robust detection and identification of honeybee infection

Honeybees, integral to global pollination and food security, confront an imminent threat posed by the Varroa destructor mite. Timely identification of Varroa infestations is pivotal for sustaining bee populations. This research introduces a pioneering strategy for early detection, harnessing cutting-edge deep learning methodologies, specifically centered around Convolutional Neural Networks (CNNs), with emphasis on ResNet and Inception models. Additionally, we deploy data augmentation techniques to refine model training. Employing Contrast Limited Adaptive Histogram Equalization (CLAHE) as a preprocessing measure enhances image quality and elevates detection precision. Our findings exhibit substantial advancements in Varroa mite identification, promising improved bee health management and fostering sustainable pollination practices. The outcomes of our experiments are promising, suggesting that our proposed approach enhances the process of identifying bee disease. This improvement holds the potential to yield superior uncovering results compared to existing methods.